User Models

1. User Models Predicting a user’s behaviour

2. Fitts’ Law

3. Objectives • Define predictive and descriptive models and explain why they are useful • Describe Fitts’ Law and explain its implications for interface design • Apply Fitts’ Law and other predictive models to evaluate interfaces • Explain Guiard’s model of two-handed interaction. Apply this model to evaluate two-handed interaction techniques

5. Trackpad Mouse

6. Fitts’ Law ID = log2(A/W + 1) MT = a + b*ID ID = Index of difficulty MT = movement time (to move hand to a target) A = amplitude (distance to target) W = width of target

8. Which is faster on average? Linear menu Pie / marking menu

9. Aside: marking menus • Selection is even faster by using a gesture • Menu doesn’t need to appear

10. Where are the fastest places to access?

11. Which is faster?

12. Why is this menu slow to use?

13. Action Analysis • Use mathematical models to predict more complex actions than pointing • Simple Example: Keystroke-Level Model (KLM) • List the steps required to complete an operation, and sum up average times for each step

14. Average Times (seconds) Physical movements: • Enter one keystroke on a standard keyboard 0.28 • Use mouse to point at an object on the screen 1.1 • Click mouse or other device 0.2 • move hand to pointing device or function key 0.4 Visual perception: • respond to a brief light 0.1 • recognize a six letter word 0.34 • move eyes to a new location on the screen 0.23 Mental Actions • retrieve a simple item from long-term memory 1.2 • learn a single “step” procedure 25 • execute a mental “step” 0.075 • Prepare for next step (choose a method) 1.35

15. Example: Bus fare boxes • List the steps needed to: • Pay your fare by coins • Validate an existing transfer • Estimate how long each willtake, on average

16. Example: Bus Fare Boxes • Fare Box 2: • Payment by coins: • Passenger tells driver how many zones. Driver presses button to indicate. • Coins dropped into slot are counted by machine. • Machine prints transfer. Fare box 1: Payment by coins: • Passenger tells driver how many zones. • Coins drop into glass box. Driver glances to see if fare seems approx. correct. • Driver tears off transfer (clip is pre-positioned so transfer will tear off with correct time shown). • Driver pushes foot pedal to drop money into box

17. Example: Bus Fare Boxes • Fare Box 2: • To validate a transfer • Passenger feeds transfer into slot. • Machine reads transfer electronically and prints ok message. • Machine returns transfer to user. Fare box 1: To validate an existing transfer • Passenger holds up for driver to see • Driver determines if time is valid

18. Expert vs. novice users • Fitts’ law and the KLM model only EXPERT performance. • Novice performance is much harder to model.

19. Predictive vs. Descriptive models • Predictive – allow a mathematical prediction of performance (usually time)e.g. Fitts’ law, KLM • Descriptive – A framework for thinking about a problem e.g. Guiard’s model

20. Guiard’s Model of Bimanual Control From Scott Mackenzie

21. Case studies • See Mackenzie reading for case studies • E.g. Text entry on mobile phones Multi-tap vs. One key + disambiguation

22. If you assume one-finger entry (e.g. thumb), can model this using Fitts’ law

23. More complex user modeling: Eg. Correctly placing menus • Problem: popup menus can be inconveniently placed on a tabletop display • May be upside down for some users • May be awkward for left-hand users

24. Solution: neural network Step 1: Training Neural network Handedness Side of table Position & orientation of input device (pen) Mark Hancock - 2003

25. Solution: neural network Step 2: Predict handedness & side of table Use this to position menu correctly Neural network Position & orientation of input device (pen) Handedness Side of table Mark Hancock - 2003

26. Key Points • Predictive models enable you to predict expert user performance at simple tasks, and consequently design interfaces that will support better performance. • Predictive models have limited usefulness (only expert users & frequent operations). They should not replace user testing. • Descriptive models may help you understand a process better.